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library(xgboost)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
library(Matrix)
library(mclust)
    __  ___________    __  _____________
   /  |/  / ____/ /   / / / / ___/_  __/
  / /|_/ / /   / /   / / / /\__ \ / /   
 / /  / / /___/ /___/ /_/ /___/ // /    
/_/  /_/\____/_____/\____//____//_/    version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")

分发训练集

Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3061
Number of edges: 99234

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9566
Number of communities: 4
Elapsed time: 0 seconds
umapplot(ds2_AC)
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6498
Number of edges: 215869

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9332
Number of communities: 3
Elapsed time: 0 seconds
umapplot(ds2_PA)


AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
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Calculating cluster 1

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Calculating cluster 2

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Calculating cluster 3

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PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
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Calculating cluster 1

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在AC上训练

AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

# data(agaricus.train, package='xgboost')

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) #这个cluster和分类没有关系


multi_featureplot(head(importance,9)$Feature,ds2)

AC_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
    pre
true           0           1           2           3
   0 0.994652406 0.002673797 0.002673797 0.000000000
   1 0.000000000 0.987755102 0.012244898 0.000000000
   2 0.000000000 0.026455026 0.962962963 0.010582011
   3 0.000000000 0.009009009 0.054054054 0.936936937
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
Warning in roc.default(AC_test_data$label, predict_AC_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

在PA上训练

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))

set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)

colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

# data(agaricus.train, pPAkage='xgboost')

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)


multi_featureplot(head(importance,9)$Feature, ds2)

PA_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop
    pre
true           0           1           2
   0 0.970982143 0.024553571 0.004464286
   1 0.033603708 0.959443801 0.006952491
   2 0.041884817 0.015706806 0.942408377
adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能
[1] 0.8821278
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
Warning in roc.default(PA_test_data$label, predict_PA_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

选择特征common genes of top 500

使用所有来自PA的细胞训练分类器

应用在AC上,计算ARI

selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)

xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)


multi_featureplot(head(importance,9)$Feature, ds2)

应用到AC上

AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹
    pre
true           0           1           2
   0 0.980360065 0.003273322 0.016366612
   1 0.799516908 0.196859903 0.003623188
   2 0.453004622 0.493066256 0.053929122
   3 0.002762431 0.052486188 0.944751381
#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
Warning in roc.default(AC_test_data$label, predict_AC_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC


# 计算ARI 

adjustedRandIndex(predict_AC_test, AC_test_data$label)
[1] 0.3024837

选择特征common genes of top 500

使用所有来自AC的细胞训练分类器

应用在PA上,计算ARI

AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)

xgb_ACram <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 200, verbose = 1)

# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)
xgb.ggplot.importance(head(importance2,20),n_clusters = 1)


multi_featureplot(head(importance2,9)$Feature, ds2)

应用到PA上

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop  #分析发育轨迹
    pre
true           0           1           2           3
   0 0.027107438 0.287272727 0.682644628 0.002975207
   1 0.000349895 0.075227432 0.914975507 0.009447166
   2 0.008130081 0.003252033 0.175609756 0.813008130
#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
Warning in roc.default(PA_test_data$label, predict_PA_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC


# 计算ARI 

adjustedRandIndex(predict_PA_test, PA_test_data$label)
[1] 0.1797689
umapplot(ds2,split.by = "conditions")
table(ds2$conditions)

sankey plot

PA -> AC ARI 0.3024837

pre

true 0 1 2 0 0.980360065 0.003273322 0.016366612 1 0.799516908 0.196859903 0.003623188 2 0.453004622 0.493066256 0.053929122 3 0.002762431 0.052486188 0.944751381 ## AC ->PA ARI 0.1797689 pre true 0 1 2 3 0 0.027107438 0.287272727 0.682644628 0.002975207 1 0.000349895 0.075227432 0.914975507 0.009447166 2 0.008130081 0.003252033 0.175609756 0.813008130


varify 部分

数据集CA_dataset1

在AC上训练 使用top500 in AC

temp <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  AC_data[i,] <- temp[i,]
}
rm(temp)

AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)


watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) #这个cluster和分类没有关系


multi_featureplot(head(importance,9)$Feature,ds2)



#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
    pre
true           0           1           2           3
   0 0.994652406 0.000000000 0.005347594 0.000000000
   1 0.000000000 0.987755102 0.012244898 0.000000000
   2 0.000000000 0.021164021 0.968253968 0.010582011
   3 0.000000000 0.000000000 0.045045045 0.954954955
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
Warning in roc.default(AC_test_data$label, predict_AC_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

temp <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)

ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL

ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)

ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)
ds1_data_confuse_matrix_test
    pre
true    0    1    2    3
   0    0   10 1400    1
   1    2  101 1232   16
   2    0   38 1086    3
   3    0    0   54  345
   4    0    0   61    0
ds1_data_confuse_matrix_test_prop  #分析发育轨迹
    pre
true            0            1            2            3
   0 0.0000000000 0.0070871722 0.9922041106 0.0007087172
   1 0.0014803849 0.0747594375 0.9119170984 0.0118430792
   2 0.0000000000 0.0337178350 0.9636202307 0.0026619343
   3 0.0000000000 0.0000000000 0.1353383459 0.8646616541
   4 0.0000000000 0.0000000000 1.0000000000 0.0000000000
#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc <- pROC::roc(ds1_test_data$label, predict_ds1_test)
Warning in roc.default(ds1_test_data$label, predict_ds1_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC


# 计算ARI 

adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
[1] 0.1136541

冠状动脉数据集

ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5401
Number of edges: 173943

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9544
Number of communities: 5
Elapsed time: 0 seconds
umapplot(ds0)

f("MYH11",ds0)

ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.5, min.diff.pct = 0.2)
Calculating cluster 0

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selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(selected_features,colnames(temp)))


for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
# rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL

ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)

ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
ds0_data_confuse_matrix_test
    pre
true    0    1    2
   0 1991    3  184
   1  101  155 1529
   2    3 1207    3
   3  172    1    0
   4   51    0    1
ds0_data_confuse_matrix_test_prop  #分析发育轨迹
    pre
true           0           1           2
   0 0.914141414 0.001377410 0.084481175
   1 0.056582633 0.086834734 0.856582633
   2 0.002473207 0.995053586 0.002473207
   3 0.994219653 0.005780347 0.000000000
   4 0.980769231 0.000000000 0.019230769
#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC
xgboost_roc <- pROC::roc(ds0_test_data$label, predict_ds0_test)
Warning in roc.default(ds0_test_data$label, predict_ds0_test) :
  'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC


# 计算ARI 

adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
[1] 0.7004005
multi_featureplot(head(importance2,9)$Feature, ds2_AC)

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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(xgboost)
library(Matrix)
library(mclust)
```

```{r}
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")
```

# 分发训练集
```{r}
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds2_AC)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds2_PA)

AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
```

## 在AC上训练
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

# data(agaricus.train, package='xgboost')

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) #这个cluster和分类没有关系

multi_featureplot(head(importance,9)$Feature,ds2)
AC_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

```


## 在PA上训练
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))

set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)

colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

# data(agaricus.train, pPAkage='xgboost')

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)

multi_featureplot(head(importance,9)$Feature, ds2)
PA_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop

adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能

#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

```
## 选择特征common genes of top 500
## 使用所有来自PA的细胞训练分类器
## 应用在AC上，计算ARI
```{r}
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)

xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)

multi_featureplot(head(importance,9)$Feature, ds2)
```
# 应用到AC上
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_AC_test, AC_test_data$label)
```

## 选择特征common genes of top 500
## 使用所有来自AC的细胞训练分类器
## 应用在PA上，计算ARI
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)

xgb_ACram <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 200, verbose = 1)

# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)
xgb.ggplot.importance(head(importance2,20),n_clusters = 1)

multi_featureplot(head(importance2,9)$Feature, ds2)
```


# 应用到PA上
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_PA_test, PA_test_data$label)
```



```{r}
umapplot(ds2,split.by = "conditions")
table(ds2$conditions)
```


# sankey plot

## PA -> AC     ARI 0.3024837
    pre
true           0           1           2
   0 0.980360065 0.003273322 0.016366612
   1 0.799516908 0.196859903 0.003623188
   2 0.453004622 0.493066256 0.053929122
   3 0.002762431 0.052486188 0.944751381
## AC ->PA    ARI 0.1797689
    pre
true           0           1           2           3
   0 0.027107438 0.287272727 0.682644628 0.002975207
   1 0.000349895 0.075227432 0.914975507 0.009447166
   2 0.008130081 0.003252033 0.175609756 0.813008130
```{r}
library(plotly)
c("PA_0", "PA_1", "PA_2", "AC_0","AC_1","AC_2","AC_3", "PA_0", "PA_1", "PA_2")
fig <- plot_ly(
    type = "sankey",
    orientation = "h",
    node = list(
      label = c("A1", "A2", "B1", "B2", "C1", "C2"),
      color = colors_list, 
      pad = 15,
      thickness = 20,
      line = list(
        color = "black",
        width = 0.5 )),
    link = list(
      source = c(0,1,0,2,3,3),
      target = c(2,3,3,4,4,5),
      value =  c(8,4,2,8,4,2)
    )
  )
fig <- fig %>% layout(
    title = "Basic Sankey Diagram",
    font = list(
      size = 10 ))

fig

```

---
# varify 部分

# 数据集CA_dataset1

## 在AC上训练  使用top500 in AC
```{r}
temp <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  AC_data[i,] <- temp[i,]
}
rm(temp)

AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)


watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) #这个cluster和分类没有关系

multi_featureplot(head(importance,9)$Feature,ds2)


#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

```



```{r}
ds1 <- ds1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.15)
umapplot(ds1)
f("FBLN1",ds1)
ds1_markers <- FindAllMarkers(ds1,logfc.threshold = 0.5, min.diff.pct = 0.2)
```

```{r}
temp <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)

ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL

ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)

ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)
ds1_data_confuse_matrix_test
ds1_data_confuse_matrix_test_prop  #分析发育轨迹



#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc <- pROC::roc(ds1_test_data$label, predict_ds1_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
```


# 冠状动脉数据集


```{r}
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds0)
f("MYH11",ds0)
ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.7, min.diff.pct = 0.2)
```

```{r}
selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(selected_features,colnames(temp)))


for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
# rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL

ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)

ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹



#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC
xgboost_roc <- pROC::roc(ds0_test_data$label, predict_ds0_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
```

```{r}
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
umapplot(ds1)
```


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